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Educ 200C Wed. Oct 3, 2012

This guide explores key statistical concepts such as variation, deviation scores, and correlation. We begin with defining what variation is and how to visualize it within a dataset. The discussion includes the significance of deviation scores, sum of squares (SS), variance, and standard deviation, illustrating their calculations and interpretations. The role of Z-scores in standardizing data points is explained, along with their use in calculating correlation coefficients. Lastly, we provide formulas for correlation analysis, enhancing prediction capabilities between related variables.

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Educ 200C Wed. Oct 3, 2012

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  1. Educ 200CWed. Oct 3, 2012

  2. Variation • What is it? • What does it look like in a data set?

  3. Deviation score • Measure the distance of each point from the mean

  4. Sum of Squares (SS) • What is SS? • When is SS large? When is SS small?

  5. Variance and Standard Deviation • Variance is Sum of Squares divided by N-1 Hard to interpret—still in “squared deviation” units • Standard deviation is the square root of the variance • Gives a measure of deviation in the units of the original observations

  6. Z-scores • Z-scores always have a mean of 0 and standard deviation of 1 • Z-scores make it easier to understand each data point (What does a z-score of 0.2 mean? What about -1.2?) • Z-scores enable us to calculate correlation coefficients.

  7. Correlation Calculation • Similar to how we calculate variation, but consider the deviation of variables from each other rather than from their mean. • Z-score product formula:

  8. What does Zx ∙Zy mean? • As an example, think of Zxand Zy as scores for an individual on two different tests. • What if Zx and Zy are both high? • What if Zx and Zy are both low? • What if they have no relation to each other? • We do this for every pair of points, add them up, and then divide by N to calculate rxy.

  9. Two more formulas (these get you the same rxy) • Z-score difference formula • Raw score formula

  10. What correlation tells you • Correlation tells us how closely related two variables are. • Also, correlation can be used for prediction • If the correlation between math and reading scores is .67, then if a math score for a student is 1 standard deviation above the mean, then we predict her reading score will be .67 above from the mean.

  11. Back to our hands data—Let’s calculate… • Mean for estimated and mean for actual • Standard deviation for both sets of data • Z-scores for each data point • Zx ∙Zyfor each pair of data points • rxy

  12. Questions?

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